ACM BPDC

🧠 Imagine | 👨🏻‍💻 Implement | 🚀 Innovate

Project Truffle

This is project Truffle. Here's how to eat it:

• Take a small bite by exploring the contents listed below.
• Be patient. Let the truffle melt as it enriches you with the knowledge of AI.
• Have another bite... We have a lot to offer!
• Start your AI journey by having a Truffle.

Clearing the Confusion: AI vs ML vs DL

Evolution of AI

📕 Table of Contents

• Programming Languages
• Integrated Development Environments (IDEs)
• Python Libraries
• Frameworks
• Courses/Tutorials
• Data Sources
• Cloud Services
• Cheat Sheets
• Blogs

📆  More topics coming soon!

🤖 Overview of Machine Learning

Data Preprocessing

1. Importing the dataset
2. Missing Data
3. Categorical Data
4. Splitting the dataset into Training and Test Set
5. Feature Scaling

Regression

1. Simple Linear Regression
2. Multi-Linear Regression
3. Polynomial Regression
4. SVR
5. Desicion Tree
6. Random Forest

Classification

1. Logistic Classification
2. K-Nearest Neigbors
3. Support Vector Machine
4. Kernel SVM
5. Naive Bayes
6. Desicion Tree
7. Random Forest

Clustering

1. K-Means Clustering
2. Hierarchical Clustering

Association Rule Learning

1. Apriori
2. Eclat

Reinforcement Learning

1. Upper Confidence Bound (UCB)
2. Thompson Sampling

Deep Learning

1. Artificial Neural Networks
2. Convolutional Neural Network

Dimensionality Reduction

1. Principal Component Analysis (PCA)
2. Linear Discriminant Analysis (LDA)
3. Kernal PCA

Model Selection & Boosting

1. K-Fold Cross Validation
2. Grid Search
3. XG-Boost

🗺️ Roadmaps

The purpose of these roadmaps is to give you an idea about the landscape and to guide you if you are confused about what to learn next and not to encourage you to pick what is hip and trendy. You should grow some understanding of why one tool would better suited for some cases than the other and remember hype and trendy never means best suited for the job.

• Data Science Roadmap
• Machine Learning Roadmap
• Deep Learning Roadmap
• Data Engineer Roadmap
• Big Data Engineer Roadmap

🎓 Higher Degrees

• Best Master's Programs
• Best Ph.D. Programs

💸 Artificial Intelligence (AI): Salaries Heading Skyward

Artificial intelligence salaries benefit from the perfect recipe for a sweet paycheck: a hot field and high demand for scarce talent. It’s the ever-reliable law of supply and demand, and right now, anything artificial intelligence-related is in very high demand.

While the average salary for a Software Engineer is around $100,000 to $150,000, to make the big bucks you want to be an AI or Machine Learning (Specialist/Scientist/Engineer). Read more about it here.

Therefore, to be an effective and in-demand AI developer, you need a lot of skills, not just one or two. Here is list of the top 10 skills you need to know for AI:

1. Machine Learning

2. Python

3. Statistics

4. Data Science

5. Hadoop

6. Big Data

7. Java

8. Data Mining

9. Spark

10. R

⚙️ Contribution Guidelines

• Contributions are always welcome!
• Please read the Contribution guidelines first.
• You can find our Contributing guidelines here.

🔐 Code Of Conduct

You can find our Code of Conduct here.

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